Pavo Enterprise

The metrics that matter,moved in weeks.

Pavo audits the surfaces where revenue leaks, then tests every fix as a controlled experiment.

See how it works

Across these surfaces

6

platform.pavoai.com/audit

Relevance audit

Intent-scored · sampled queries

4 tracked

“leather jacket racer”search → results
42%low relevance
“wireless earbuds pro”search → results
81%
“running shoes size 10”search → results
53%low relevance
“gift under $50”search → results
88%

Low-relevance queries · 2 of 4

Opportunity foundRelevance42/100

What’s leaking

Results match keywords but miss intent—‘leather jacket racer’ returns café-racer books, not jackets.

The fix

Rerank via hybrid retrieval and contextual embeddings.

Coverage audit

Catalog reach · per rail

4 tracked

Homepage rail3 / 3 same category
12%narrow
PDP also-viewednarrow catalog
18%narrow
Cart cross-sellno discovery
9%narrow
Category topsome spread
34%

Catalog surfaced · 12%

Opportunity foundCatalog coverage12%

What’s leaking

Rails keep showing the same category, which caps cross-sell and discovery.

The fix

Inject diversity constraints and controlled exploration.

Elasticity audit

Willingness-to-pay · per SKU

4 tracked

Wireless Earbuds Pro$79 → $69 · +12% vol
34%mispriced
Leather Weekender$240 → hold
72%
Organic Face Serum$34 → $38 · +9% margin
41%mispriced
Running Shoes v3$120 → $99 · +22% vol
29%mispriced

Mispriced SKUs · 3 of 4

Opportunity foundElasticity fit44/100

What’s leaking

Markdown rules ignore willingness-to-pay—you over-discount high-intent buyers and under-discount fence-sitters.

The fix

Per-segment price elasticity and promotion timing.

Incrementality audit

Organic overlap · geo-validated

4 tracked

High-intent searchersGoogle · $18k/mo
88%
Repeat buyersMeta · $24k/mo
22%waste
Lookalike convertersMeta · $14k/mo
36%waste
New vertical explorersTikTok · $8k/mo
91%

Non-incremental spend · $38k/mo recoverable

Opportunity foundIncrementality0.8×

What’s leaking

Campaigns can’t separate incremental lift from organic intent, so you pay for conversions that would have happened regardless.

The fix

Incrementality-scored audiences and budget reallocation.

Agent run audit

Golden-set scored · traces sampled

4 tracked

Order status and returns12k runs/wk
88%
Product Q&A9k runs/wk
54%stalling
Seller onboarding3k runs/wk
47%stalling
Checkout assistance6k runs/wk
73%

Flows below task-success bar · 2 of 4

Opportunity foundTask success71%

What’s leaking

Support and shopping agents handle the happy path but stall on edge cases, escalating to humans or answering wrong, with no eval to catch it.

The fix

Trace every run, score against a golden set, gate releases.

How it works

Pavo starts by learning how you actually work.Then it tests every approach worth trying.

Pavo works inside the systems and workflows you already run—your team reviews every change before it ships.

The operating model

One metric. Multiple approaches. A faster path to production.

Pavo starts from your business objective. Over 8 weeks, a Pavo scientist embeds with your team to evaluate multiple approaches in parallel and prove the winner in a live A/B.

Stage

Leaves behind

W1W2W3W4W5W6W7W8

01
Week 1A Pavo scientist works inside your team at this decision
Target metric defined
We agree the one metric you want to move and what a win looks like, before anyone touches a model.
Leaves behind
Metric definition · success criteria · guardrails
02
Week 2
Baseline established
We connect to your systems and map how they work today, so there’s a real baseline to beat.
Leaves behind
Tribal knowledge consolidation · connected stack · scope locked
03
Weeks 3–4A Pavo scientist works inside your team at this decision
Candidates evaluated in parallel
We try several approaches at once and let the results rank them, so success doesn’t ride on one bet.
Leaves behind
Candidate set · offline harness
04
Weeks 3–4A Pavo scientist works inside your team at this decision
Winner selected offline
We score every approach offline first, so only the winner reaches your users.
Leaves behind
Offline evaluation · offline→online correlation
05
Weeks 5–6A Pavo scientist works inside your team at this decision
Live A/B in production
You sign off, then we ship the winner as a live A/B on real traffic.
Leaves behind
Live A/B · signed-off checkpoint
06
Weeks 7–8
Lift measured, loop repeated
You see the lift in production, keep the whole system, and point it at your next problem.
Leaves behind
Confirmed lift · expand to next metric or surface

Evidence returns to W1

A Pavo scientist works inside your team at each decision—the metric, the evaluation, and the production call.

See the Enterprise trial

The numbers they moved

Teams that moved the number that mattered.

Both quotes come from one 25M-user consumer learning app, where the growth and content teams own the metrics Pavo moved.

Compounding advantage

The first win makes the next one faster.

Every experiment leaves behind reusable context, evaluations, skills, integrations, decisions, and production evidence. Teams stop rebuilding the same understanding for each project.

Artifact	Stage 01 One production system Search and recommendations	Carried Stage 02 Additional surfaces Further ranking surfaces	Carried Stage 03 Enterprise-wide Adjacent systems
Context	01Built	02	03
Evaluations	01Built	02Reused	03Reused
Skills	01Built	02Reused	03Reused
Integration	01Built	02Reused	03Reused
Surface context	01	02Built	03
Experiment history	01	02	03Reused
System context	01	02	03Built
Setup	01Full setup	02Partial setup	03Minimal setup

Reuse over rebuild

Security and trust

The security posture your compliance team wants

View Trust Center

ISO 27001
Certified
SOC 2 Type II
Compliant

Encryption
Encrypted in transit and at rest, with managed production keys.
Access control
Least-privilege access, enforced with MFA and reviewed quarterly.
Data control
Tenant-segmented, with retention and customer-controlled deletion.
Monitoring and response
Continuously monitored, centrally logged, and ready to respond.
Tested and patched
Independently pen-tested, scanned, and kept current against threats.
Resilient by design
Multi-AZ with backups and a tested continuity and recovery plan.

Full reports, policies, and the complete control list are available on request through the Trust Center.

The Enterprise trial

One important system.
One measurable objective.
One production improvement loop.

What the trial contains

01Problem-formulation workshop
02Agreed success criteria
03Platform and context setup
04Existing-stack integration plan
05Initial candidate approaches
06Offline evaluation
07Production experiment recommendation
08Expansion plan if the trial succeeds

The metrics that matter,moved in weeks.

Pavo starts by learning how you actually work.Then it tests every approach worth trying.

Capture tribal knowledge

Design the approaches

Test in parallel

One metric. Multiple approaches. A faster path to production.

Target metric defined

Baseline established

Candidates evaluated in parallel

Winner selected offline

Live A/B in production

Lift measured, loop repeated

Teams that moved the number that mattered.

The first win makes the next one faster.

The security posture your compliance team wants

Encryption

Access control

Data control

Monitoring and response

Tested and patched

Resilient by design

One important system.
One measurable objective.
One production improvement loop.

The metrics that matter,moved in weeks.

Pavo starts by learning how you actually work.Then it tests every approach worth trying.

Capture tribal knowledge

Design the approaches

Test in parallel

One metric. Multiple approaches. A faster path to production.

Target metric defined

Baseline established

Candidates evaluated in parallel

Winner selected offline

Live A/B in production

Lift measured, loop repeated

Teams that moved the number that mattered.

The first win makes the next one faster.

The security posture your compliance team wants

Encryption

Access control

Data control

Monitoring and response

Tested and patched

Resilient by design

One important system.One measurable objective.One production improvement loop.

One important system.
One measurable objective.
One production improvement loop.